基于ICSM-UKF算法的UCAV目标状态估计  

Target State Estimation for UCAV Based on ICSM-UKF

作　　者：丁达理[1,2] 罗建军[1] 宋磊[2] 马卫华[1] 

机构地区：[1]西北工业大学航天学院,西安710072 [2]空军工程大学航空航天工程学院,西安710038

出　　处：《电光与控制》2012年第11期1-6,共6页Electronics Optics & Control

基　　金：光电控制技术重点实验室和航空科学基金联合资助项目(20105196016);国家自然科学基金(61004124)

摘　　要：针对在UCAV对运动目标状态估计时,"当前"统计模型(Current Statistical Model,CSM)中加速度上下限在采样周期内为常数的不合理性,应用模糊自适应控制理论,提出了一种改进的"当前"统计模型(Improved Current Statistical Model,ICSM),给出了模糊隶属度函数;对无迹卡尔曼滤波(Unscented Kalman Filter,UKF)不具有应对量测噪声统计不精确或未知的自适应性,提出了一种带量测噪声统计估计器的自适应UKF算法;将ICSM-UKF算法与基于"当前"统计模型的EKF算法进行了对比仿真,仿真结果表明该算法具有滤波精度高、稳定性强的优点。When Unmanned Combat Aerial Vehicle （UCAV） estimates the target state, the upper and lower limits of the acceleration during the sampling time are constant for Current Statistical Model （CSM）, which is irrational. To solve the problem, an Improved Current Statistical Model （ ICSM ） was proposed using the fuzzy adaptive control theory, and the fuzzy subject function was put forward. Considering that the Unscented Kalman Filter（UKF） is not adaptive to the imprecise or unknown measurement noise, we proposed an adaptive UKF algorithm with estimator of measurement noise statistics. The ICSM-UKF algorithm was compared with the EKF based on CSM by simulation. The results show that this algorithm has the advantages of high precision and strong stability.

关 键 词：无人作战飞机 “当前”统计模型 无迹卡尔曼滤波 自适应 

分 类 号：V271.4[航空宇航科学与技术—飞行器设计] TP273[自动化与计算机技术—检测技术与自动化装置]